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[SPARK-23053][CORE][BRANCH-2.1] taskBinarySerialization and task partitions calculate in DagScheduler.submitMissingTasks should keep the same RDD checkpoint status #20635

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[SPARK-23053][CORE][BRANCH-2.1] taskBinarySerialization and task partitions calculate in DagScheduler.submitMissingTasks should keep the same RDD checkpoint status #20635

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27 changes: 18 additions & 9 deletions core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ import org.apache.spark.executor.TaskMetrics
import org.apache.spark.internal.Logging
import org.apache.spark.network.util.JavaUtils
import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult}
import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.{RDD, RDDCheckpointData}
import org.apache.spark.rpc.RpcTimeout
import org.apache.spark.storage._
import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
Expand Down Expand Up @@ -982,15 +982,24 @@ class DAGScheduler(
// might modify state of objects referenced in their closures. This is necessary in Hadoop
// where the JobConf/Configuration object is not thread-safe.
var taskBinary: Broadcast[Array[Byte]] = null
var partitions: Array[Partition] = null
try {
// For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep).
// For ResultTask, serialize and broadcast (rdd, func).
val taskBinaryBytes: Array[Byte] = stage match {
case stage: ShuffleMapStage =>
JavaUtils.bufferToArray(
closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
case stage: ResultStage =>
JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
var taskBinaryBytes: Array[Byte] = null
// taskBinaryBytes and partitions are both effected by the checkpoint status. We need
// this synchronization in case another concurrent job is checkpointing this RDD, so we get a
// consistent view of both variables.
RDDCheckpointData.synchronized {
taskBinaryBytes = stage match {
case stage: ShuffleMapStage =>
JavaUtils.bufferToArray(
closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef))
case stage: ResultStage =>
JavaUtils.bufferToArray(closureSerializer.serialize((stage.rdd, stage.func): AnyRef))
}

partitions = stage.rdd.partitions
}

taskBinary = sc.broadcast(taskBinaryBytes)
Expand All @@ -1013,7 +1022,7 @@ class DAGScheduler(
case stage: ShuffleMapStage =>
partitionsToCompute.map { id =>
val locs = taskIdToLocations(id)
val part = stage.rdd.partitions(id)
val part = partitions(id)
new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
taskBinary, part, locs, stage.latestInfo.taskMetrics, properties, Option(jobId),
Option(sc.applicationId), sc.applicationAttemptId)
Expand All @@ -1022,7 +1031,7 @@ class DAGScheduler(
case stage: ResultStage =>
partitionsToCompute.map { id =>
val p: Int = stage.partitions(id)
val part = stage.rdd.partitions(p)
val part = partitions(p)
val locs = taskIdToLocations(id)
new ResultTask(stage.id, stage.latestInfo.attemptId,
taskBinary, part, locs, id, properties, stage.latestInfo.taskMetrics,
Expand Down